Overview

Dataset statistics

Number of variables17
Number of observations23446
Missing cells130260
Missing cells (%)32.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory144.0 B

Variable types

Categorical1
DateTime1
Numeric14
Unsupported1

Alerts

spec_conductance is highly overall correlated with pHHigh correlation
DO is highly overall correlated with mean_tempHigh correlation
pH is highly overall correlated with spec_conductanceHigh correlation
mean_temp is highly overall correlated with DOHigh correlation
CH4_conc is highly overall correlated with CO2_concHigh correlation
CO2_conc is highly overall correlated with CH4_concHigh correlation
nitrate_mean has 9397 (40.1%) missing valuesMissing
spec_conductance has 3833 (16.3%) missing valuesMissing
DO has 4002 (17.1%) missing valuesMissing
sealevelDO_sat has 4005 (17.1%) missing valuesMissing
pH has 3992 (17.0%) missing valuesMissing
chlorophyll has 3910 (16.7%) missing valuesMissing
turbidity has 3858 (16.5%) missing valuesMissing
fDOM has 5629 (24.0%) missing valuesMissing
mean_temp has 2912 (12.4%) missing valuesMissing
CH4_conc has 21975 (93.7%) missing valuesMissing
CO2_conc has 21975 (93.7%) missing valuesMissing
N2O_conc has 21975 (93.7%) missing valuesMissing
Microbialabundanceper_ml has 22797 (97.2%) missing valuesMissing
spec_conductance is highly skewed (γ1 = 20.20168481)Skewed
chlorophyll is highly skewed (γ1 = 46.37723597)Skewed
mean_temp is highly skewed (γ1 = 103.4869704)Skewed
N2O_conc is highly skewed (γ1 = 27.46463877)Skewed
year_month is an unsupported type, check if it needs cleaning or further analysisUnsupported
spectrum_count has 9458 (40.3%) zerosZeros

Reproduction

Analysis started2023-02-13 20:27:45.332256
Analysis finished2023-02-13 20:28:06.341465
Duration21.01 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

siteid
Categorical

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.3 KiB
ARIK
 
1096
LECO
 
1096
WALK
 
1096
SYCA
 
1096
PRIN
 
1096
Other values (19)
17966 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters93784
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARIK
2nd rowARIK
3rd rowARIK
4th rowARIK
5th rowARIK

Common Values

ValueCountFrequency (%)
ARIK 1096
 
4.7%
LECO 1096
 
4.7%
WALK 1096
 
4.7%
SYCA 1096
 
4.7%
PRIN 1096
 
4.7%
POSE 1096
 
4.7%
MCRA 1096
 
4.7%
MCDI 1096
 
4.7%
MAYF 1096
 
4.7%
LEWI 1096
 
4.7%
Other values (14) 12486
53.3%

Length

2023-02-13T15:28:06.404355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arik 1096
 
4.7%
mayf 1096
 
4.7%
como 1096
 
4.7%
leco 1096
 
4.7%
king 1096
 
4.7%
wlou 1096
 
4.7%
lewi 1096
 
4.7%
hopb 1096
 
4.7%
mcdi 1096
 
4.7%
mcra 1096
 
4.7%
Other values (14) 12486
53.3%

Most occurring characters

ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93784
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 93784
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 8547
 
9.1%
I 7767
 
8.3%
E 7644
 
8.2%
L 7012
 
7.5%
O 6968
 
7.4%
A 6922
 
7.4%
R 6837
 
7.3%
M 5451
 
5.8%
B 4692
 
5.0%
P 4247
 
4.5%
Other values (11) 27697
29.5%

date
Date

Distinct1096
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size366.3 KiB
Minimum2018-01-01 00:00:00
Maximum2020-12-31 00:00:00
2023-02-13T15:28:06.498657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:06.577417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

nitrate_mean
Real number (ℝ)

Distinct11521
Distinct (%)82.0%
Missing9397
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean22.590674
Minimum-1
Maximum851.7
Zeros3
Zeros (%)< 0.1%
Negative546
Negative (%)2.3%
Memory size366.3 KiB
2023-02-13T15:28:06.671842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.20851648
Q12.64375
median6.19375
Q322.146875
95-th percentile170.13312
Maximum851.7
Range852.7
Interquartile range (IQR)19.503125

Descriptive statistics

Standard deviation45.782385
Coefficient of variation (CV)2.0266055
Kurtosis17.592005
Mean22.590674
Median Absolute Deviation (MAD)4.9447917
Skewness3.6129915
Sum317376.38
Variance2096.0268
MonotonicityNot monotonic
2023-02-13T15:28:06.750857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 47
 
0.2%
-0.9 10
 
< 0.1%
3.633333333 7
 
< 0.1%
1.7 6
 
< 0.1%
3.3 6
 
< 0.1%
3.8 6
 
< 0.1%
2.854166667 6
 
< 0.1%
2.7 6
 
< 0.1%
-0.9666666667 6
 
< 0.1%
6.5 6
 
< 0.1%
Other values (11511) 13943
59.5%
(Missing) 9397
40.1%
ValueCountFrequency (%)
-1 47
0.2%
-0.9983050847 1
 
< 0.1%
-0.9925925926 1
 
< 0.1%
-0.9913043478 1
 
< 0.1%
-0.9882352941 1
 
< 0.1%
-0.9857142857 1
 
< 0.1%
-0.9854545455 1
 
< 0.1%
-0.9851351351 1
 
< 0.1%
-0.98 3
 
< 0.1%
-0.975 3
 
< 0.1%
ValueCountFrequency (%)
851.7 1
< 0.1%
493.6 1
< 0.1%
284.4541667 1
< 0.1%
264.3375 1
< 0.1%
261.76875 1
< 0.1%
261.3 1
< 0.1%
260.85625 1
< 0.1%
260.5569231 1
< 0.1%
259.7614583 1
< 0.1%
259.6916667 1
< 0.1%

spec_conductance
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct19600
Distinct (%)99.9%
Missing3833
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean559.3787
Minimum-0.15208333
Maximum215585.2
Zeros0
Zeros (%)0.0%
Negative129
Negative (%)0.6%
Memory size366.3 KiB
2023-02-13T15:28:06.860749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.15208333
5-th percentile13.001953
Q137.487733
median118.86168
Q3508.93932
95-th percentile687.7591
Maximum215585.2
Range215585.35
Interquartile range (IQR)471.45159

Descriptive statistics

Standard deviation6009.0504
Coefficient of variation (CV)10.742365
Kurtosis426.35157
Mean559.3787
Median Absolute Deviation (MAD)100.46622
Skewness20.201685
Sum10971094
Variance36108686
MonotonicityNot monotonic
2023-02-13T15:28:06.939473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117989.8333 6
 
< 0.1%
117992.3333 3
 
< 0.1%
117973.3333 3
 
< 0.1%
117990.8333 2
 
< 0.1%
117989.3333 2
 
< 0.1%
117978.3333 2
 
< 0.1%
117988.3333 2
 
< 0.1%
630.3183681 1
 
< 0.1%
20.71520081 1
 
< 0.1%
616.9035382 1
 
< 0.1%
Other values (19590) 19590
83.6%
(Missing) 3833
 
16.3%
ValueCountFrequency (%)
-0.1520833333 1
< 0.1%
-0.1425098641 1
< 0.1%
-0.1304875097 1
< 0.1%
-0.1262824675 1
< 0.1%
-0.1221186274 1
< 0.1%
-0.1182964219 1
< 0.1%
-0.1166371419 1
< 0.1%
-0.1155133484 1
< 0.1%
-0.1117497485 1
< 0.1%
-0.1114399395 1
< 0.1%
ValueCountFrequency (%)
215585.1969 1
< 0.1%
118086.5914 1
< 0.1%
118034.1458 1
< 0.1%
118032.4041 1
< 0.1%
118026.3421 1
< 0.1%
118017.1726 1
< 0.1%
118014.5285 1
< 0.1%
118010.4898 1
< 0.1%
118006.4988 1
< 0.1%
118001.9287 1
< 0.1%

DO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19403
Distinct (%)99.8%
Missing4002
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean10.471025
Minimum-5.65
Maximum354.97759
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size366.3 KiB
2023-02-13T15:28:07.030213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-5.65
5-th percentile4.8833549
Q18.2590243
median9.7186718
Q311.150961
95-th percentile13.215627
Maximum354.97759
Range360.62759
Interquartile range (IQR)2.8919366

Descriptive statistics

Standard deviation16.137639
Coefficient of variation (CV)1.5411709
Kurtosis316.13212
Mean10.471025
Median Absolute Deviation (MAD)1.4494398
Skewness17.493341
Sum203598.6
Variance260.42338
MonotonicityNot monotonic
2023-02-13T15:28:07.272472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.14142361 2
 
< 0.1%
10.40619792 2
 
< 0.1%
9.959465278 2
 
< 0.1%
8.368774306 2
 
< 0.1%
5.68025 2
 
< 0.1%
8.635027778 2
 
< 0.1%
7.737868056 2
 
< 0.1%
11.36145486 2
 
< 0.1%
10.39339931 2
 
< 0.1%
7.833145833 2
 
< 0.1%
Other values (19393) 19424
82.8%
(Missing) 4002
 
17.1%
ValueCountFrequency (%)
-5.65 1
< 0.1%
-4.22 1
< 0.1%
-3.4275 1
< 0.1%
0.2234930556 1
< 0.1%
0.2289583333 1
< 0.1%
0.2346944444 1
< 0.1%
0.535875 1
< 0.1%
0.5426909722 1
< 0.1%
0.5442743056 1
< 0.1%
0.5455208333 1
< 0.1%
ValueCountFrequency (%)
354.9775903 1
< 0.1%
350.844588 1
< 0.1%
350.2903487 1
< 0.1%
348.9448664 1
< 0.1%
345.6860347 1
< 0.1%
344.8827361 1
< 0.1%
344.8493507 1
< 0.1%
343.2838435 1
< 0.1%
342.7693021 1
< 0.1%
339.5286148 1
< 0.1%

sealevelDO_sat
Real number (ℝ)

Distinct19428
Distinct (%)99.9%
Missing4005
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean85.074722
Minimum-0.21
Maximum149.14118
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)< 0.1%
Memory size366.3 KiB
2023-02-13T15:28:07.368056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.21
5-th percentile49.103212
Q179.177274
median91.297566
Q395.705566
95-th percentile100.54755
Maximum149.14118
Range149.35118
Interquartile range (IQR)16.528292

Descriptive statistics

Standard deviation17.183113
Coefficient of variation (CV)0.20197672
Kurtosis5.5205545
Mean85.074722
Median Absolute Deviation (MAD)5.8905039
Skewness-2.1003772
Sum1653937.7
Variance295.25937
MonotonicityNot monotonic
2023-02-13T15:28:07.454142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.62247569 2
 
< 0.1%
93.98849306 2
 
< 0.1%
95.57340972 2
 
< 0.1%
93.19398958 2
 
< 0.1%
94.63985764 2
 
< 0.1%
61.44536458 2
 
< 0.1%
94.83295139 2
 
< 0.1%
98.59803472 2
 
< 0.1%
95.78164583 2
 
< 0.1%
96.51315972 2
 
< 0.1%
Other values (19418) 19421
82.8%
(Missing) 4005
 
17.1%
ValueCountFrequency (%)
-0.21 2
< 0.1%
-0.19 1
< 0.1%
-0.14 1
< 0.1%
-0.115 1
< 0.1%
-0.11 1
< 0.1%
-0.04 1
< 0.1%
0.02 1
< 0.1%
2.288666667 1
< 0.1%
2.297881944 1
< 0.1%
2.308645833 1
< 0.1%
ValueCountFrequency (%)
149.1411765 1
< 0.1%
148.9765208 1
< 0.1%
139.1314931 1
< 0.1%
137.623235 1
< 0.1%
122.77996 1
< 0.1%
122.3434653 1
< 0.1%
120.336537 1
< 0.1%
119.5040933 1
< 0.1%
119.3185 1
< 0.1%
118.3817465 1
< 0.1%

pH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19296
Distinct (%)99.2%
Missing3992
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean7.2566606
Minimum-148.42144
Maximum180.16242
Zeros0
Zeros (%)0.0%
Negative140
Negative (%)0.6%
Memory size366.3 KiB
2023-02-13T15:28:07.573889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-148.42144
5-th percentile5.7261533
Q17.1420248
median7.6251458
Q37.9781875
95-th percentile8.3359217
Maximum180.16242
Range328.58386
Interquartile range (IQR)0.83616267

Descriptive statistics

Standard deviation6.7242064
Coefficient of variation (CV)0.92662545
Kurtosis278.3843
Mean7.2566606
Median Absolute Deviation (MAD)0.40646354
Skewness-8.4017295
Sum141171.08
Variance45.214952
MonotonicityNot monotonic
2023-02-13T15:28:07.709573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.965013889 3
 
< 0.1%
0.04 3
 
< 0.1%
8.123048611 3
 
< 0.1%
7.329017361 2
 
< 0.1%
7.977076389 2
 
< 0.1%
7.985086806 2
 
< 0.1%
7.336493056 2
 
< 0.1%
7.305177083 2
 
< 0.1%
8.080402778 2
 
< 0.1%
8.049201389 2
 
< 0.1%
Other values (19286) 19431
82.9%
(Missing) 3992
 
17.0%
ValueCountFrequency (%)
-148.421441 1
< 0.1%
-115.6593542 1
< 0.1%
-110.3337083 1
< 0.1%
-106.9238056 1
< 0.1%
-105.0351042 1
< 0.1%
-104.8037535 1
< 0.1%
-104.7387639 1
< 0.1%
-104.5517639 1
< 0.1%
-104.5385 1
< 0.1%
-104.5080174 1
< 0.1%
ValueCountFrequency (%)
180.1624167 1
< 0.1%
178.2650833 1
< 0.1%
176.5400653 1
< 0.1%
175.7289896 1
< 0.1%
72.91786458 1
< 0.1%
56.03611111 1
< 0.1%
54.62387153 1
< 0.1%
54.36675 1
< 0.1%
54.31365278 1
< 0.1%
53.59072569 1
< 0.1%

chlorophyll
Real number (ℝ)

MISSING  SKEWED 

Distinct19389
Distinct (%)99.2%
Missing3910
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean41.104601
Minimum-12043.857
Maximum59462.899
Zeros105
Zeros (%)0.4%
Negative1132
Negative (%)4.8%
Memory size366.3 KiB
2023-02-13T15:28:07.845530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-12043.857
5-th percentile-0.020173248
Q10.4556276
median1.2474192
Q34.2909149
95-th percentile102.58862
Maximum59462.899
Range71506.755
Interquartile range (IQR)3.8352873

Descriptive statistics

Standard deviation1024.7074
Coefficient of variation (CV)24.929264
Kurtosis2343.3533
Mean41.104601
Median Absolute Deviation (MAD)1.0461337
Skewness46.377236
Sum803019.49
Variance1050025.3
MonotonicityNot monotonic
2023-02-13T15:28:07.972550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
0.4%
0.05965972222 2
 
< 0.1%
0.04934027778 2
 
< 0.1%
0.1066527778 2
 
< 0.1%
0.5601041667 2
 
< 0.1%
0.01877777778 2
 
< 0.1%
0.06297222222 2
 
< 0.1%
0.1680972222 2
 
< 0.1%
0.8604027778 2
 
< 0.1%
0.0165 2
 
< 0.1%
Other values (19379) 19413
82.8%
(Missing) 3910
 
16.7%
ValueCountFrequency (%)
-12043.8566 1
< 0.1%
-4668.906243 1
< 0.1%
-3208.867799 1
< 0.1%
-3205.274562 1
< 0.1%
-3052.578439 1
< 0.1%
-2807.758331 1
< 0.1%
-1645.977826 1
< 0.1%
-1478.561921 1
< 0.1%
-1290.922361 1
< 0.1%
-1183.380573 1
< 0.1%
ValueCountFrequency (%)
59462.8986 1
< 0.1%
58945.07041 1
< 0.1%
54365.8061 1
< 0.1%
53169.06921 1
< 0.1%
47121.93829 1
< 0.1%
35084.85759 1
< 0.1%
31630.10413 1
< 0.1%
27480.42869 1
< 0.1%
26948.5032 1
< 0.1%
25381.5971 1
< 0.1%

turbidity
Real number (ℝ)

Distinct19572
Distinct (%)99.9%
Missing3858
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean57.285627
Minimum-27301.296
Maximum11043.457
Zeros1
Zeros (%)< 0.1%
Negative3383
Negative (%)14.4%
Memory size366.3 KiB
2023-02-13T15:28:08.099331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-27301.296
5-th percentile-3.7620175
Q10.50782551
median1.8504705
Q39.3409201
95-th percentile207.67363
Maximum11043.457
Range38344.753
Interquartile range (IQR)8.8330946

Descriptive statistics

Standard deviation422.82847
Coefficient of variation (CV)7.381057
Kurtosis1086.686
Mean57.285627
Median Absolute Deviation (MAD)2.5844201
Skewness-3.7208688
Sum1122110.9
Variance178783.92
MonotonicityNot monotonic
2023-02-13T15:28:08.198479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9552673611 2
 
< 0.1%
2.869972222 2
 
< 0.1%
7.893503472 2
 
< 0.1%
0.525625 2
 
< 0.1%
2.357190972 2
 
< 0.1%
1.072895833 2
 
< 0.1%
1.455454861 2
 
< 0.1%
1.389895833 2
 
< 0.1%
1.531225694 2
 
< 0.1%
1.080548611 2
 
< 0.1%
Other values (19562) 19568
83.5%
(Missing) 3858
 
16.5%
ValueCountFrequency (%)
-27301.29578 1
< 0.1%
-6862.923271 1
< 0.1%
-3195.010385 1
< 0.1%
-1250.391348 1
< 0.1%
-950.2015139 1
< 0.1%
-724.3873333 1
< 0.1%
-698.9657569 1
< 0.1%
-652.7575521 1
< 0.1%
-103.1050837 1
< 0.1%
-60.69411458 1
< 0.1%
ValueCountFrequency (%)
11043.45684 1
< 0.1%
10727.07897 1
< 0.1%
10554.77311 1
< 0.1%
10257.78202 1
< 0.1%
10022.43098 1
< 0.1%
9699.25307 1
< 0.1%
9003.368333 1
< 0.1%
8361.376756 1
< 0.1%
7901.04174 1
< 0.1%
7672.896052 1
< 0.1%

fDOM
Real number (ℝ)

Distinct17797
Distinct (%)99.9%
Missing5629
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean25.211668
Minimum-181.64178
Maximum683.04393
Zeros0
Zeros (%)0.0%
Negative1577
Negative (%)6.7%
Memory size366.3 KiB
2023-02-13T15:28:08.291754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-181.64178
5-th percentile-1.5130534
Q14.5213097
median13.594332
Q332.539527
95-th percentile83.437978
Maximum683.04393
Range864.68572
Interquartile range (IQR)28.018217

Descriptive statistics

Standard deviation39.751483
Coefficient of variation (CV)1.5767097
Kurtosis49.98873
Mean25.211668
Median Absolute Deviation (MAD)11.541332
Skewness5.2986381
Sum449196.29
Variance1580.1804
MonotonicityNot monotonic
2023-02-13T15:28:08.374613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.234263889 2
 
< 0.1%
15.45 2
 
< 0.1%
0.6370763889 2
 
< 0.1%
17.15 2
 
< 0.1%
4.34 2
 
< 0.1%
16.25 2
 
< 0.1%
0.5123611111 2
 
< 0.1%
6.532826389 2
 
< 0.1%
-2.55 2
 
< 0.1%
3.26 2
 
< 0.1%
Other values (17787) 17797
75.9%
(Missing) 5629
 
24.0%
ValueCountFrequency (%)
-181.6417847 1
< 0.1%
-68.17801389 1
< 0.1%
-67.88182639 1
< 0.1%
-67.8706875 1
< 0.1%
-67.79990278 1
< 0.1%
-67.43822222 1
< 0.1%
-67.30304167 1
< 0.1%
-67.29336806 1
< 0.1%
-66.59643056 1
< 0.1%
-66.52503472 1
< 0.1%
ValueCountFrequency (%)
683.0439306 1
< 0.1%
636.10975 1
< 0.1%
615.15625 1
< 0.1%
610.4070737 1
< 0.1%
605.4689167 1
< 0.1%
598.6687222 1
< 0.1%
587.8141319 1
< 0.1%
587.4855278 1
< 0.1%
583.7215229 1
< 0.1%
556.1297315 1
< 0.1%

spectrum_count
Real number (ℝ)

Distinct2891
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.146151
Minimum0
Maximum197.09306
Zeros9458
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-13T15:28:08.466477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19.173611
Q320
95-th percentile50
Maximum197.09306
Range197.09306
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.208483
Coefficient of variation (CV)1.4374004
Kurtosis11.926772
Mean16.146151
Median Absolute Deviation (MAD)19.173611
Skewness3.0788834
Sum378562.65
Variance538.63369
MonotonicityNot monotonic
2023-02-13T15:28:08.550646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9458
40.3%
20 8234
35.1%
50 1158
 
4.9%
19.79166667 91
 
0.4%
4 74
 
0.3%
19.98958333 54
 
0.2%
19.58333333 42
 
0.2%
19.375 37
 
0.2%
0.2083333333 33
 
0.1%
19.97916667 32
 
0.1%
Other values (2881) 4233
18.1%
ValueCountFrequency (%)
0 9458
40.3%
0.0003472222222 2
 
< 0.1%
0.0006944444444 1
 
< 0.1%
0.001388888889 1
 
< 0.1%
0.006944444444 1
 
< 0.1%
0.009375 1
 
< 0.1%
0.01041666667 6
 
< 0.1%
0.01319444444 1
 
< 0.1%
0.01388888889 1
 
< 0.1%
0.01666666667 1
 
< 0.1%
ValueCountFrequency (%)
197.0930556 1
< 0.1%
191.2104167 1
< 0.1%
189.0201389 1
< 0.1%
183.2590278 1
< 0.1%
182.9736111 1
< 0.1%
182.7326389 1
< 0.1%
151.8131944 1
< 0.1%
150.5180556 1
< 0.1%
149.18125 1
< 0.1%
149.0194444 1
< 0.1%

mean_temp
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct20525
Distinct (%)> 99.9%
Missing2912
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean973.65777
Minimum-766.49623
Maximum10890572
Zeros0
Zeros (%)0.0%
Negative1156
Negative (%)4.9%
Memory size366.3 KiB
2023-02-13T15:28:08.633094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-766.49623
5-th percentile-0.015114418
Q14.6626947
median10.885894
Q317.897768
95-th percentile24.157748
Maximum10890572
Range10891338
Interquartile range (IQR)13.235073

Descriptive statistics

Standard deviation96425.395
Coefficient of variation (CV)99.034176
Kurtosis10859.034
Mean973.65777
Median Absolute Deviation (MAD)6.6263984
Skewness103.48697
Sum19993089
Variance9.2978569 × 109
MonotonicityNot monotonic
2023-02-13T15:28:08.742150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.21115625 2
 
< 0.1%
-0.003340277778 2
 
< 0.1%
19.03614931 2
 
< 0.1%
2.846267361 2
 
< 0.1%
10.28518403 2
 
< 0.1%
9.052541667 2
 
< 0.1%
7.199010417 2
 
< 0.1%
7.678208333 2
 
< 0.1%
14.58721701 2
 
< 0.1%
10.39513542 1
 
< 0.1%
Other values (20515) 20515
87.5%
(Missing) 2912
 
12.4%
ValueCountFrequency (%)
-766.4962326 1
< 0.1%
-716.2207535 1
< 0.1%
-502.5662847 1
< 0.1%
-292.9203194 1
< 0.1%
-288.4305451 1
< 0.1%
-281.5613958 1
< 0.1%
-279.7228021 1
< 0.1%
-276.7377326 1
< 0.1%
-276.0710642 1
< 0.1%
-268.0691076 1
< 0.1%
ValueCountFrequency (%)
10890571.77 1
< 0.1%
8498233.042 1
< 0.1%
326642.3714 1
< 0.1%
28115.55292 1
< 0.1%
15321.26237 1
< 0.1%
10638.33609 1
< 0.1%
473.5789218 1
< 0.1%
428.1937934 1
< 0.1%
342.9852569 1
< 0.1%
261.1095434 1
< 0.1%

year_month
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size366.3 KiB

CH4_conc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1471
Distinct (%)100.0%
Missing21975
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean14.229912
Minimum0.98494075
Maximum1795.692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-13T15:28:08.879005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.98494075
5-th percentile1.7719437
Q12.2462129
median2.9154175
Q36.537037
95-th percentile43.872397
Maximum1795.692
Range1794.7071
Interquartile range (IQR)4.2908241

Descriptive statistics

Standard deviation69.405083
Coefficient of variation (CV)4.8774076
Kurtosis350.57312
Mean14.229912
Median Absolute Deviation (MAD)0.95267747
Skewness16.350438
Sum20932.201
Variance4817.0655
MonotonicityNot monotonic
2023-02-13T15:28:08.973802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1144756 1
 
< 0.1%
2.06407175 1
 
< 0.1%
1.723966 1
 
< 0.1%
1.7741433 1
 
< 0.1%
2.2400565 1
 
< 0.1%
2.0337949 1
 
< 0.1%
1.99267845 1
 
< 0.1%
1.94605775 1
 
< 0.1%
1.9652251 1
 
< 0.1%
2.1604141 1
 
< 0.1%
Other values (1461) 1461
 
6.2%
(Missing) 21975
93.7%
ValueCountFrequency (%)
0.98494075 1
< 0.1%
1.14367275 1
< 0.1%
1.1456277 1
< 0.1%
1.1575606 1
< 0.1%
1.1919986 1
< 0.1%
1.2387358 1
< 0.1%
1.25669625 1
< 0.1%
1.2924489 1
< 0.1%
1.3088367 1
< 0.1%
1.33234565 1
< 0.1%
ValueCountFrequency (%)
1795.692045 1
< 0.1%
1099.988239 1
< 0.1%
658.8522525 1
< 0.1%
625.1824908 1
< 0.1%
522.2641439 1
< 0.1%
507.196405 1
< 0.1%
446.3837544 1
< 0.1%
413.8323269 1
< 0.1%
343.2472323 1
< 0.1%
327.7892796 1
< 0.1%

CO2_conc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1471
Distinct (%)100.0%
Missing21975
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean1270.005
Minimum451.65826
Maximum12480.499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-13T15:28:09.210092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum451.65826
5-th percentile573.72323
Q1721.63625
median919.05709
Q31300.3328
95-th percentile3236.6187
Maximum12480.499
Range12028.841
Interquartile range (IQR)578.69653

Descriptive statistics

Standard deviation1182.4906
Coefficient of variation (CV)0.93109129
Kurtosis25.410466
Mean1270.005
Median Absolute Deviation (MAD)242.33339
Skewness4.4291735
Sum1868177.3
Variance1398284
MonotonicityNot monotonic
2023-02-13T15:28:09.304541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
697.1785662 1
 
< 0.1%
941.6299162 1
 
< 0.1%
923.5231602 1
 
< 0.1%
620.2914126 1
 
< 0.1%
710.8935514 1
 
< 0.1%
612.0101796 1
 
< 0.1%
552.3072795 1
 
< 0.1%
694.8649211 1
 
< 0.1%
724.939462 1
 
< 0.1%
632.2737526 1
 
< 0.1%
Other values (1461) 1461
 
6.2%
(Missing) 21975
93.7%
ValueCountFrequency (%)
451.658259 1
< 0.1%
465.3519152 1
< 0.1%
471.3964059 1
< 0.1%
487.1799353 1
< 0.1%
488.551533 1
< 0.1%
488.6288924 1
< 0.1%
496.7575391 1
< 0.1%
498.0666387 1
< 0.1%
498.330625 1
< 0.1%
503.2026078 1
< 0.1%
ValueCountFrequency (%)
12480.49888 1
< 0.1%
11310.68249 1
< 0.1%
11306.36511 1
< 0.1%
10007.02552 1
< 0.1%
9344.265345 1
< 0.1%
9115.481392 1
< 0.1%
8602.955041 1
< 0.1%
8539.139887 1
< 0.1%
8470.422789 1
< 0.1%
8467.017459 1
< 0.1%

N2O_conc
Real number (ℝ)

MISSING  SKEWED 

Distinct1471
Distinct (%)100.0%
Missing21975
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean0.64287659
Minimum0.1848145
Maximum50.816405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-13T15:28:09.400517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1848145
5-th percentile0.32119398
Q10.43221318
median0.5223865
Q30.6644633
95-th percentile1.0237796
Maximum50.816405
Range50.63159
Interquartile range (IQR)0.23225012

Descriptive statistics

Standard deviation1.5812658
Coefficient of variation (CV)2.4596725
Kurtosis804.53926
Mean0.64287659
Median Absolute Deviation (MAD)0.114229
Skewness27.464639
Sum945.67146
Variance2.5004016
MonotonicityNot monotonic
2023-02-13T15:28:09.510686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.55989035 1
 
< 0.1%
0.42812065 1
 
< 0.1%
0.50948535 1
 
< 0.1%
0.5682762 1
 
< 0.1%
0.4120205 1
 
< 0.1%
0.4341768 1
 
< 0.1%
0.43389515 1
 
< 0.1%
0.41211845 1
 
< 0.1%
0.44212555 1
 
< 0.1%
0.50498115 1
 
< 0.1%
Other values (1461) 1461
 
6.2%
(Missing) 21975
93.7%
ValueCountFrequency (%)
0.1848145 1
< 0.1%
0.21246945 1
< 0.1%
0.21812315 1
< 0.1%
0.22561035 1
< 0.1%
0.2277536 1
< 0.1%
0.2387321 1
< 0.1%
0.24403455 1
< 0.1%
0.2460871 1
< 0.1%
0.2520539 1
< 0.1%
0.25247145 1
< 0.1%
ValueCountFrequency (%)
50.8164046 1
< 0.1%
32.67810045 1
< 0.1%
6.1993433 1
< 0.1%
2.86720455 1
< 0.1%
2.70986205 1
< 0.1%
2.5122613 1
< 0.1%
2.4494767 1
< 0.1%
2.41977485 1
< 0.1%
2.1035131 1
< 0.1%
1.997072 1
< 0.1%

Microbialabundanceper_ml
Real number (ℝ)

Distinct600
Distinct (%)92.4%
Missing22797
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean1883205.5
Minimum3361.1111
Maximum62606310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.3 KiB
2023-02-13T15:28:09.636301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3361.1111
5-th percentile43114
Q1155075.56
median313511.11
Q3832772.22
95-th percentile9140058
Maximum62606310
Range62602949
Interquartile range (IQR)677696.67

Descriptive statistics

Standard deviation6284089.7
Coefficient of variation (CV)3.3369113
Kurtosis46.321372
Mean1883205.5
Median Absolute Deviation (MAD)207674.44
Skewness6.2900801
Sum1.2222004 × 109
Variance3.9489783 × 1013
MonotonicityNot monotonic
2023-02-13T15:28:09.714552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295747.7778 4
 
< 0.1%
75857.77778 3
 
< 0.1%
165637.7778 3
 
< 0.1%
192524.4444 3
 
< 0.1%
576132.2222 3
 
< 0.1%
458984.4444 3
 
< 0.1%
1254046.667 2
 
< 0.1%
184842.2222 2
 
< 0.1%
191563.3333 2
 
< 0.1%
2467764.444 2
 
< 0.1%
Other values (590) 622
 
2.7%
(Missing) 22797
97.2%
ValueCountFrequency (%)
3361.111111 1
< 0.1%
4548.888889 1
< 0.1%
9002.222222 1
< 0.1%
9048.888889 1
< 0.1%
12447.77778 1
< 0.1%
12975.55556 1
< 0.1%
13443.33333 1
< 0.1%
14207.77778 1
< 0.1%
16585.55556 1
< 0.1%
17764.44444 1
< 0.1%
ValueCountFrequency (%)
62606310 1
< 0.1%
61982167.78 1
< 0.1%
59389574.44 1
< 0.1%
39993141.11 1
< 0.1%
39849108.89 1
< 0.1%
39465021.11 1
< 0.1%
35361682.22 1
< 0.1%
32692272.22 1
< 0.1%
32397805.56 1
< 0.1%
30390946.67 1
< 0.1%

Interactions

2023-02-13T15:28:04.376717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:46.499533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.011736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.318077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.909334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.136973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.694821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.412131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.622988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.054968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.422002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.734630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.907642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.228714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.438822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:46.641199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.137029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.413105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.035717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.263518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.916784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.490369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.701135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.164793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.500232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.837690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.001863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.306948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.533839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:46.751349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.278756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.539657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.130216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.389209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.042846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.584734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.889722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.243541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.578744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.900194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.065094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.385665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.643976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:46.845737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.373204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.664568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.224074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.482962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.152777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.647854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.016087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.322254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.817962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.979095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.143830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.448385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.727107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:46.924447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.451452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.791101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.302805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.610171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.278434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.742121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.112369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.400389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.909155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.042186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.363466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.526927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.958442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.003096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.530272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.916163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.380925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.750879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.388288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.820750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.252547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.479047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.988047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.120836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.457646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.636916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.038221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.065701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.641125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.073731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.459659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.876294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.530348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.915128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.331471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.573213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.066892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.183594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.536786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.731182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.115448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.196192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.735874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.184128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.538492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.986585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.638375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.993268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.393974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.651535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.161310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.262350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.615019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.841344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.193722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.295516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.845398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.263846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.617129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.065454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.782189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.087965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.472628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.745764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.240058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.373213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.693625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.903859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.303700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.350425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.924134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.341965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.711484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.159565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:54.892706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.198169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.567123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.855733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.318689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.467258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.788083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.982599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.382053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.428967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:48.999131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.404860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.774389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.285875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.019247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.276300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.661021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:58.997199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.381193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.546132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:02.897974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.076705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.445249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.523467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.066226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.515185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.853051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.427440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.108096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.402692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.755720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.139135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.491165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.687982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.009013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.155606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.507852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.649621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.160581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.625206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:51.938114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.538196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.207953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.465791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.865809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.248465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.585499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.766231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.087272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.234200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:05.586483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:47.868921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:49.239341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:50.704159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:52.010615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:53.616307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:55.285985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:56.544461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:57.976413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:27:59.320104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:00.648716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:01.829406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:03.150471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-13T15:28:04.296816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-13T15:28:09.793276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
nitrate_meanspec_conductanceDOsealevelDO_satpHchlorophyllturbidityfDOMspectrum_countmean_tempCH4_concCO2_concN2O_concMicrobialabundanceper_mlsiteid
nitrate_mean1.0000.411-0.0790.2290.3330.2880.2950.1390.0310.2900.2380.2190.0250.0610.441
spec_conductance0.4111.000-0.282-0.1800.6380.2460.0470.1320.0960.2500.3750.4870.0820.0970.113
DO-0.079-0.2821.0000.472-0.145-0.159-0.163-0.0500.042-0.719-0.323-0.440-0.261-0.2810.111
sealevelDO_sat0.229-0.1800.4721.000-0.032-0.186-0.041-0.269-0.0790.087-0.211-0.248-0.137-0.2280.410
pH0.3330.638-0.145-0.0321.0000.1610.045-0.0270.1140.1400.0780.2000.056-0.0580.141
chlorophyll0.2880.246-0.159-0.1860.1611.0000.3420.291-0.0180.0610.2600.1940.0270.1640.036
turbidity0.2950.047-0.163-0.0410.0450.3421.0000.1030.0380.2100.2560.1930.0080.2940.055
fDOM0.1390.132-0.050-0.269-0.0270.2910.1031.0000.058-0.1120.2920.042-0.0250.3690.147
spectrum_count0.0310.0960.042-0.0790.114-0.0180.0380.0581.000-0.099-0.0090.0700.0440.0280.206
mean_temp0.2900.250-0.7190.0870.1400.0610.210-0.112-0.0991.0000.3110.4120.1640.1630.000
CH4_conc0.2380.375-0.323-0.2110.0780.2600.2560.292-0.0090.3111.0000.6380.1060.3560.122
CO2_conc0.2190.487-0.440-0.2480.2000.1940.1930.0420.0700.4120.6381.0000.2270.1910.277
N2O_conc0.0250.082-0.261-0.1370.0560.0270.008-0.0250.0440.1640.1060.2271.000-0.0610.047
Microbialabundanceper_ml0.0610.097-0.281-0.228-0.0580.1640.2940.3690.0280.1630.3560.191-0.0611.0000.124
siteid0.4410.1130.1110.4100.1410.0360.0550.1470.2060.0000.1220.2770.0470.1241.000

Missing values

2023-02-13T15:28:05.712185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T15:28:05.916611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-13T15:28:06.153047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

siteiddatenitrate_meanspec_conductanceDOsealevelDO_satpHchlorophyllturbidityfDOMspectrum_countmean_tempyear_monthCH4_concCO2_concN2O_concMicrobialabundanceper_ml
0ARIK2018-01-01NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN2018-01NaNNaNNaNNaN
1ARIK2018-01-02NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN2018-01NaNNaNNaNNaN
2ARIK2018-01-0313.700000539.05944410.18638970.7602787.8225002.0950002.899444NaN0.0-3.6768002018-01NaNNaNNaNNaN
3ARIK2018-01-0412.535417546.4800429.92178568.9982227.8124033.24946560.369840NaN0.0-3.5588782018-0121.7656651340.942660.46566452674.444444
4ARIK2018-01-0510.310417550.8302089.72172967.7189587.8267435.5652362.548007NaN0.0-3.3135832018-01NaNNaNNaNNaN
5ARIK2018-01-069.966667535.34356910.06052870.3843477.8392228.42205633.219167NaN0.0-3.0848752018-01NaNNaNNaNNaN
6ARIK2018-01-079.207292503.49446510.33370173.4936877.8834799.2041673.417035NaN0.0-2.4703822018-01NaNNaNNaNNaN
7ARIK2018-01-088.103125486.38858210.64500074.6670767.90935412.09032619.39932645.220.0-2.7804902018-01NaNNaNNaNNaN
8ARIK2018-01-097.708333475.40259210.65071576.0212647.92991043.9297364.278028NaN0.0-2.1278372018-01NaNNaNNaNNaN
9ARIK2018-01-106.932292467.58849310.57774375.6787297.94794430.26864611.884910NaN0.0-1.9688472018-01NaNNaNNaNNaN
siteiddatenitrate_meanspec_conductanceDOsealevelDO_satpHchlorophyllturbidityfDOMspectrum_countmean_tempyear_monthCH4_concCO2_concN2O_concMicrobialabundanceper_ml
23436WLOU2020-12-223.808333110.89398310.41450771.6914348.0930031.3441671.8892784.58103520.0000000.1811582020-12NaNNaNNaNNaN
23437WLOU2020-12-233.822917111.12055610.40830971.3528828.1041460.9894414.4692334.63829920.0000000.0395432020-12NaNNaNNaNNaN
23438WLOU2020-12-243.650000111.48186910.49597671.9198578.1035000.9282382.3897504.5185000.2149310.0207502020-12NaNNaNNaNNaN
23439WLOU2020-12-25NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN
23440WLOU2020-12-26NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN
23441WLOU2020-12-27NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN
23442WLOU2020-12-28NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN
23443WLOU2020-12-29NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN
23444WLOU2020-12-30NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN
23445WLOU2020-12-31NaNNaNNaNNaNNaNNaNNaNNaN0.000000NaN2020-12NaNNaNNaNNaN